Getting Started with AI

See what the Splunk AI Assistant can do for you https://youtu.be/ynRBiepXVw4?si=eua38GwP0nwuvk3m

The Splunk App for Anomaly Detection finds anomalies in time series datasets and provides an end-to-end workflow to manage and operationalize anomaly detection tasks. The app detects seasonal patterns and finds anomalies in just a couple of clicks. Using the app, you can create anomaly detection jobs, run these jobs on a regular cadence, view SPL queries, and create alerts. The app works with any time series dataset that can be ingested into the Splunk platform, provided the points in the time series are evenly spaced. The app uses machine learning to detect seasonality in the data without user inputs, lowering the barriers to realizing value. The app also performs health diagnostics on the time series to check whether the dataset is suitable for anomaly detection using our algorithm. If the dataset is not fit for anomaly detection with our algorithm, we have included steps to preprocess the data in the docs. (We are working on supporting more time series out of the box without preprocessing steps for the next version). When you create an anomaly detection job, the app generates an SPL query you can view and use elsewhere within Splunk. Similar to other Splunk applications, the resource consumption of CPU and memory is commensurate with the size of the datasets that you use. This app is available for both on-premise and cloud customers.

* Description * Smart Alerts Assistant for Splunk (beta) enables analysts to focus on the most important and highest-priority issues. Under the hood, Smart Alerts Assistant for Splunk captures user feedback to improve prioritization and uses machine learning to output a recommended, stack-ranked priority for each alert. Smart Alerts Assistant for Splunk empowers customers to optimize their operations centers and focus on top priorities instead of spending time triaging alerts. * Setup * See the Details tab for dependencies and setup instructions. * Help * While this app is not supported by Splunk, the development team can be reached at mlsupport@splunk.com or via the splunk-usergroups Slack in the #machinelearning channel. Responses are made on a best effort basis. Feedback is always welcome and appreciated! Learn more about the splunk-usergroups Slack here: https://docs.splunk.com/Documentation/Community/current/community/Chat#Join_us_on_Slack) This beta release is solely for internal testing and evaluation and not for production use.

The Splunk AI Toolkit delivers new SPL commands, custom visualizations, assistants, and examples to explore a variety of ML concepts. Each assistant includes end-to-end examples with datasets, plus the ability to apply the visualizations and SPL commands to your own data. You can inspect the assistant panels and underlying code to see how it all works. AI Toolkit Quick Reference Guide: https://docs.splunk.com/images/3/3f/Splunk-MLTK-QuickRefGuide-2019-web.pdf Generative AI: With release 5.6, AI Toolkit supports Generative AI, allowing customers to integrate Large Language Models (LLMs) and Time Series Foundational Models directly into their search pipelines. Agentic Capabilities: With release 6.0.0, AI Toolkit introduces Agent Launchpad, enabling customers to create AI agents directly within the app, connect their own MCP servers, Knowledge bases and build agentic workflows. Assistants: * Predict Numeric Fields (Linear Regression): e.g. predict median house values. * Predict Categorical Fields (Logistic Regression): e.g. predict customer churn. * Detect Numeric Outliers (distribution statistics): e.g. detect outliers in IT Ops data. * Detect Categorical Outliers (probabilistic measures): e.g. detect outliers in diabetes patient records. * Forecast Time Series: e.g. forecast data center growth and capacity planning. * Cluster Numeric Events: e.g. cluster business anomalies to reduce noise. Smart Assistants (new assistants with revamped UI and better ml pipeline/experiment management): * Smart Forecasting Assistant:: e.g. forecasting app logons with special days. * Smart Outlier Detection Assistant: e.g. find anomalies in supermarket purchases. * Smart Clustering Assistant: e.g. cluster houses by property descriptions. * Smart Prediction Assistant: e.g. predict vulnerabilities in firewall data.

This add-on contains a Python interpreter bundled with the following scientific and machine learning libraries: numpy, scipy, pandas, scikit-learn, and statsmodels. With this add-on, you can import these powerful libraries in your own custom search commands, custom rest endpoints, modular inputs, and so forth. This add-on is available for Linux (64-bit and 32-bit), Windows (64-bit) and Mac. Make sure you install the appropriate one for your Splunk deployment.

This add-on contains a Python interpreter bundled with the following scientific and machine learning libraries: numpy, scipy, pandas, scikit-learn, and statsmodels. With this add-on, you can import these powerful libraries in your own custom search commands, custom rest endpoints, modular inputs, and so forth. This add-on is available for Linux (64-bit), Windows (64-bit) and Mac. Make sure you install the appropriate one for your Splunk deployment.

This add-on contains a Python interpreter bundled with the following scientific and machine learning libraries: numpy, scipy, pandas, scikit-learn, and statsmodels. With this add-on, you can import these powerful libraries in your own custom search commands, custom rest endpoints, modular inputs, and so forth. This add-on is available for Linux (64-bit), Windows (64-bit) and Mac. Make sure you install the appropriate one for your Splunk deployment.

The Splunk App for Data Science and Deep Learning (DSDL), formerly known as the Deep Learning Toolkit (DLTK), lets you integrate advanced custom machine learning and deep learning systems with the Splunk platform. DSDL extends the Splunk AI Toolkit (AITK), formerly known as the Machine Learning Toolkit (MLTK), with prebuilt Docker containers for TensorFlow, PyTorch, and a collection of data science, NLP, machine learning and advanced AI libraries. By using predefined workflows for rapid development with Jupyter Lab Notebooks, DSDL enables you to build, test, and operationalize your custom AI models with Splunk. You can leverage GPUs for compute intense training tasks or LLM inference and flexibly deploy models on CPU or GPU enabled containers. The app ships with 40+ examples that showcase different artificial intelligence, deep learning and machine learning algorithms for classification, regression, forecasting, clustering, graph analytics, and natural language processing tasks. Learn from these examples to tackle your most advanced data science and custom AI use cases in the areas of IT Operations, Security, Application Development, IoT, Business Analytics, and beyond.